code: def get_advantages_and_returns(self, values, rewards, start):
      # Adopted from https://github.com/CarperAI/trlx/blob/main/trlx/models/modeling_ppo.py#L134
      lastgaelam = 0
      advantages_reversed = []
      length = rewards.size()[-1]
      for t in reversed(range(start, length)):
          nextvalues = values[:, t + 1] if t < length - 1 else 0.0
          delta = rewards[:, t] + self.gamma * nextvalues - values[:, t]
          lastgaelam = delta + self.gamma * self.lam * lastgaelam
          advantages_reversed.append(lastgaelam)
      advantages = torch.stack(advantages_reversed[::-1], dim=1)
      returns = advantages + values[:, start:]
      return advantages.detach(), returns
  
imports: import torch
  import torch.nn.functional
  import time
  import deepspeed
  from deepspeed.runtime.zero.partition_parameters import ZeroParamStatus
  from deepspeed.accelerator import get_accelerator
  from dschat.utils.utils import print_rank_0
label_desc: Use image node_id 9 for calling the DeepSpeedPPOTrainer obj's underlying member method code with example usage: obj.get_advantages_and_returns(values, rewards, start) and returns: advantages,  returns